2020 International Conference on Intelligent Systems and Computer Vision (ISCV)最新文献_第10页

LMI approach to Robust Fuzzy H∞ Control for Wind Generator System in Finite Frequency Domain 有限频域风力发电系统鲁棒模糊H∞控制的LMI方法

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204248

Kaoutar Lahmadi, I. Boumhidi

{"title":"LMI approach to Robust Fuzzy H∞ Control for Wind Generator System in Finite Frequency Domain","authors":"Kaoutar Lahmadi, I. Boumhidi","doi":"10.1109/ISCV49265.2020.9204248","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204248","url":null,"abstract":"This paper deals with quandary of wind turbine control for Takagi Sugeno fuzzy model (TS) in finite frequency domain. The objective is to design a controller which can the asymptotic stability of the global system and minimize the perturbances level caused by the wind haste. The TS fuzzy model is proposed to deal with a nonlinear deportment of wind system, and the finite frequency approach sanctions the command in a specific domain of frequency. By utilizing the lemma of generalized Kalman-Yakubovich-Popuv (GKYP), the H$infty$ control theory and Linear Matrix Inequality technique (LMI), an incipient approach for the robust fuzzy control in finite frequency domain is given. When the disturbances of the systems occur in a range of finite frequencies which is known in advance, it is better to control the system on a very precise frequency domain and not over the whole range of frequencies, to obtain more efficient results and more conservative. In comparison with the full frequency control, the specific domain of frequency approach proves the better performances in wind turbine control. All the developed results are presented in the format of linear matrix inequalities (LMIs) and the simulation results were given provides satisfactory of the proposed approach.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128893757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic Oriented Text Clustering Based on RDF 基于RDF的面向语义的文本聚类

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204133

Soukaina Fatimi, Chama El Saili, L. Alaoui

{"title":"Semantic Oriented Text Clustering Based on RDF","authors":"Soukaina Fatimi, Chama El Saili, L. Alaoui","doi":"10.1109/ISCV49265.2020.9204133","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204133","url":null,"abstract":"Text clustering is the discipline that purports to find related groups in a collection of documents. Based on text clustering the use of documents can be more salubrious. Researchers have used various methods to implement text clustering either agglomerative, divisive, or itemsets-based clustering. Most of these proposed approaches do not take into account the semantic relationships between words, in this case, the documents are considered only as bags of unrelated words. Our work aims to consider the semantics of the text phrases in the clustering task, and to get full usage and exploitation of documents. The semantic web concept is overloaded with valuable techniques allowing the significant use of documents. Our goal is to take full advantage of these techniques. Using the Resource Description Framework (RDF) to represent textual data as triplets. They provide a semantic representation of data on which the clustering process will be based, to provide a more efficient clustering system. On the other hand, and based on the clustering process, we opt on incorporating other techniques such as ontology representation using RDF, RDF Schemas (RDFS), and Web Ontology Language (OWL) to manipulate and extract meaningful information. In this paper, we propose a framework of semantic oriented text clustering based on RDF by the means of a semantic similarity measure, and we highlight the benefits of using semantic web techniques in clustering, topic modeling, and information extraction based on questioning, reasoning and inferencing processes.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125655270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Amazigh isolated word speech recognition system using the Adaptive Orthogonal Transform Method. 基于自适应正交变换的Amazigh孤立词语音识别系统。

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204291

Fadwa Abakarim, A. Abenaou

引用次数: 3

Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization 基于卷积神经网络和GradCAM可视化的小麦病害分类与定位

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204258

E. Ennadifi, S. Laraba, Damien Vincke, B. Mercatoris, B. Gosselin

{"title":"Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization","authors":"E. Ennadifi, S. Laraba, Damien Vincke, B. Mercatoris, B. Gosselin","doi":"10.1109/ISCV49265.2020.9204258","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204258","url":null,"abstract":"The world has been witnessing a population boom that has several implications including food security. Wheat is one of the world’s most important crops in terms of production and consumption, and demand for it is increasing. On the other hand, diseases can damage the abundance and the quality of the crop, so this needs to be revealed through advanced methods. In recent years, along with the various technological developments, using Convolutional Neural Networks (CNN) has proved to be showing great results in many image classification tasks. However, deep learning models are generally considered as black boxes and it is difficult to understand what the model has learned. The purpose of this article is to detect diseases from wheat images using CNNs and to use visualization methods to understand what these models have learned. For this reason, a wheat database has been collected by CRA-W (Walloon Agricultural Research Center), which contains 1163 images and is classified into two groups namely sick and healthy. Moreover, we propose to use the mask R-CNN for segmentation and extraction of wheat spikes from the background. Furthermore, a visualization and interpretation method, namely Gradient-weighted Class Activation Mapping (GradCAM), is used to locate the disease on the wheat spikes in a non-supervised way. GradCAM is actually used generally to highlight the most important regions from the CNN model’s viewpoint that are used to perform the classification.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125055393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Human Factor: A Key Element in A Fire Safety System Of Hydrocarbon Storage Tank 人的因素:油气储罐消防安全系统的关键因素

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-06-01 DOI: 10.1109/ISCV49265.2020.9204292

Amel Hammouya, R. Chaib

{"title":"Human Factor: A Key Element in A Fire Safety System Of Hydrocarbon Storage Tank","authors":"Amel Hammouya, R. Chaib","doi":"10.1109/ISCV49265.2020.9204292","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204292","url":null,"abstract":"Risk analysis and assessment are extremely important in the oil sector to prevent potential risks. The objective of this work is to propose a thorough risk analysis approach with the integration of the human factor, since he is considered as a weak point of the system and a performance and safety limiter. This study is based on a combination of three tools: SADT, BORA method and ALOHA software. Each of these tools has a function in the proposed approach. Thus, SADT breaks down the studied system and determines the contribution of each component. The functions of the barriers, the success and fault scenarios and the causes of an initial event have been defined by BORA, so the consequences of safety system fault have been studied by ALOHA software. The safety system of the crude oil tank (S106) at the Skikda storage terminal in Algeria was considered as a case study. Based on the results of this research, it was determined that the function fault of a barrier in this system can produce a dangerous situation where humans can be a major cause of these faults since they contribute most of the functions of this system. The originality of this in-depth analysis is the identification of the weak points of a hydrocarbon storage tank's safety system with the integration of the human being where his role and the consequences of their error are determined.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127413421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection and Classification of Industrial Signal Lights for Factory Floors 车间工业信号灯的检测与分类

2020 International Conference on Intelligent Systems and Computer Vision (ISCV) Pub Date : 2020-04-23 DOI: 10.1109/ISCV49265.2020.9204045

Felix Nilsson, J. Jakobsen, F. Alonso-Fernandez

{"title":"Detection and Classification of Industrial Signal Lights for Factory Floors","authors":"Felix Nilsson, J. Jakobsen, F. Alonso-Fernandez","doi":"10.1109/ISCV49265.2020.9204045","DOIUrl":"https://doi.org/10.1109/ISCV49265.2020.9204045","url":null,"abstract":"Industrial manufacturing has developed during the last decades from a labor-intensive manual control of machines to a fully-connected automated process. The next big leap is known as industry 4.0, or smart manufacturing. With industry 4.0 comes increased integration between IT systems and the factory floor from the customer order system to final delivery of the product. One benefit of this integration is mass production of individually customized products. However, this has proven challenging to implement into existing factories, considering that their lifetime can be up to 30 years. The single most important parameter to measure in a factory is the operating hours of each machine. Operating hours can be affected by machine maintenance as well as re-configuration for different products. For older machines without connectivity, the operating state is typically indicated by signal lights of green, yellow and red colours. Accordingly, the goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor. Using methods commonly employed for traffic light recognition in autonomous cars, a system with an accuracy of over 99% in the specified conditions is presented. It is believed that if more diverse video data becomes available, a system with high reliability that generalizes well could be developed using a similar methodology.","PeriodicalId":313743,"journal":{"name":"2020 International Conference on Intelligent Systems and Computer Vision (ISCV)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4